Update README.md

README.md

@@ -37,6 +37,49 @@ This Hugging Face dataset repository provides PTMint-derived tables including a
   - Benchmarking models that predict PTM-dependent PPI regulation (enhance/inhibit)
   - PTM sites mapped to structural interfaces
 
+## Quickstart Usage
+### Install HuggingFace Datasets package
+Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
+First, from the command line install the `datasets` library
+
+    $ pip install datasets
+
+then, from within python load the datasets library
+
+    >>> import datasets
+
+### Load Dataset
+
+Load PTMint dataset.
+
+    >>> ptmint = datasets.load_dataset('RosettaCommons/PTMint')
+    Downloading readme: 4.83kB [00:00, 9.11MB/s]
+    Downloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 17.9M/17.9M [00:01<00:00, 17.4MB/s]
+    Downloading data: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 2.17M/2.17M [00:00<00:00, 6.04MB/s]
+    Generating ptmint_general split: 100%|█████████████████████████████████████████████████████████████████████████| 5156/5156 [00:00<00:00, 32354.87 examples/s]
+    Generating ptmint_phospho_interfaces split: 100%|████████████████████████████████████████████████████████████████| 639/639 [00:00<00:00, 33504.52 examples/s]
+    
+and the dataset is loaded as a `datasets.arrow_dataset.Dataset`
+  
+    >>> ptmint
+    DatasetDict({
+        ptmint_general: Dataset({
+            features: ['Organism', 'Gene', 'Uniprot', 'PTM', 'Site', 'AA', 'SequenceWindow', 'Int_uniprot', 'Int_gene', 'Effect', 'Method', 'Disease', 'Co-localized', 'PMID', 'Co_localized', 'Complex', 'Origin', 'Confidence', 'PDBRES', 'Score', 'Interface', 'Domain', 'interface_sites_num', 'interface_sequence_dict', 'hf_complex_path', 'Gene_sequence', 'Int_gene_sequence', 'Interactor_chain_1_from_complex', 'Interactor_1_seq_from_complex', 'Interactor_chain_2_from_complex', 'Interactor_2_seq_from_complex', 'Alternative_Complex', 'site_in_interface', 'cluster_ID', 'split'],
+            num_rows: 5156
+        })
+        ptmint_phospho_interfaces: Dataset({
+            features: ['Organism', 'Gene', 'Uniprot', 'PTM', 'Site', 'AA', 'SequenceWindow', 'Int_uniprot', 'Int_gene', 'Effect', 'Method', 'Disease', 'Co-localized', 'PMID', 'Co_localized', 'Complex', 'Origin', 'Confidence', 'PDBRES', 'Score', 'Interface', 'Domain', 'interface_sites_num', 'interface_sequence_dict', 'hf_complex_path', 'Gene_sequence', 'Int_gene_sequence', 'Interactor_chain_1_from_complex', 'Interactor_1_seq_from_complex', 'Interactor_chain_2_from_complex', 'Interactor_2_seq_from_complex', 'Alternative_Complex', 'site_in_interface', 'cluster_ID', 'split'],
+            num_rows: 639
+        })
+    })
+
+which is a column oriented format that can be accessed directly, converted in to a `pandas.DataFrame`, or `parquet` format, e.g.
+    
+    >>> ptmint.data.column('Gene')
+    >>> ptmint.to_pandas()
+    >>> ptmint.to_parquet("dataset.parquet")
+
+
 ## Citation
 
 When referring to **PTMint**, please cite: